초록 ▼

A multi-kernel neural network computing architecture configured to learn correlations among feature values 34, 38 as the network monitors and imputes measured input values 30 and also predicts future output values 46. This computing architecture includes a multi-kernel neural network array 14 with t

A multi-kernel neural network computing architecture configured to learn correlations among feature values 34, 38 as the network monitors and imputes measured input values 30 and also predicts future output values 46. This computing architecture includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.

대표청구항 ▼

A multi-kernel neural network computing architecture configured to learn correlations among feature values 34, 38 as the network monitors and imputes measured input values 30 and also predicts future output values 46. This computing architecture includes a multi-kernel neural network array 14 with t

A multi-kernel neural network computing architecture configured to learn correlations among feature values 34, 38 as the network monitors and imputes measured input values 30 and also predicts future output values 46. This computing architecture includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.